Attenuating seismic noise via incoherent dictionary learning

Abstract

Abstract We propose to apply an incoherent dictionary learning algorithm for reducing random noise in seismic data. The image denoising algorithm based on incoherent dictionary learning is proposed for solving the problem of losing partial texture information using traditional image denoising methods. The noisy image is firstly divided into different image patches, and those patches are extracted for dictionary learning. Then, we introduce the incoherent dictionary learning technology to update the dictionary. Finally, sparse representation problem is solved to obtain sparse representation coefficients by sparse coding algorithm. The denoised data can be obtained by reconstructing the image using the sparse coefficients. Application of the incoherent dictionary learning method to seismic images presents successful performance and demonstrates its superiority to the state-of-the-art denoising methods.